Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning

Deep neural networks (DNN) have been widely used in wind power forecasting (WPF), however, they still encounter difficulties such as delayed variable selection, DNN architecture design, and network weight optimization. Therefore, an ensemble cascaded DNN (ECDNN) with surrogate-assisted evolutionary neural architecture search (SEANS) and many-objective federated learning (MOFL) is proposed and referred to as ECDNNSENAS-MOFL. SEANS is proposed to determine the delayed variables and network architecture efficiently, which allows significantly enhancing the evolutionary search efficiency than traditional evolutionary neural architecture search (ENAS) approaches by employing a surrogate model to achieve fast fitness evaluations. Meanwhile, MOFL is developed by embedding the federated learning into many-objective optimization to obtain more reliable network weights based on the distributed modeling data than traditional centralized learning and federated learning, as well as solving the problems of data privacy protection and data islands. Furthermore, selective ensemble learning is introduced to build high-performance ensemble WPF model by combining the merits of multiple highly influential Pareto solutions. The effectiveness and superiority of the proposed ECDNNSENAS-MOFL method are verified using an actual wind power dataset. The application results show that the proposed method can provide new insights into data-driven WPF modeling and has a great potential for practical applications.